The answer is to change the auto-scaling metric to 'InvocationsPerInstance' because high p99 latency variability in SageMaker endpoints is typically driven by queuing and cold starts during traffic spikes, not by compute load. When you scale on average CPU utilization, the metric lags behind sudden request bursts, causing requests to queue up and inflate tail latency. Scaling on invocations per instance ensures new instances are provisioned proactively based on the actual request rate per running instance, directly addressing the root cause of the p99 latency variability. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding of real-time inference optimization and the difference between reactive and proactive scaling metrics—a common trap is assuming CPU or memory metrics are sufficient for latency-sensitive workloads. Remember: for tail latency, think "queue, not compute"—scale on invocations to keep queues short and p99 stable.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A team deployed a SageMaker endpoint for real-time inference using a PyTorch model. After monitoring, they notice that the latency is highly variable, with p99 latency 10x the p50 latency. The endpoint uses a single ml.c5.2xlarge instance with auto-scaling based on average CPU utilization. Which change is most likely to reduce latency variability?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Change the auto-scaling metric to 'InvocationsPerInstance'
Option C is correct because high p99 latency often results from cold starts or queueing when traffic spikes. Scaling on invocations per instance ensures more instances are ready. Option A (GPU) may not help if model is CPU-bound. Option B (batch size) can increase latency. Option D (warm-up) helps cold starts but not queueing.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Increase the batch size for inference
Why it's wrong here
Larger batches increase per-request latency.
✗
Pre-warm the model by sending dummy requests every minute
Why it's wrong here
Warm-up helps cold starts but not the variability due to traffic spikes.
✗
Switch to a GPU instance type
Why it's wrong here
GPU may not reduce tail latency if the bottleneck is request queueing.
✓
Change the auto-scaling metric to 'InvocationsPerInstance'
Why this is correct
Scaling on invocations per instance prevents overload and reduces queueing.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Change the auto-scaling metric to 'InvocationsPerInstance' — Option C is correct because high p99 latency often results from cold starts or queueing when traffic spikes. Scaling on invocations per instance ensures more instances are ready. Option A (GPU) may not help if model is CPU-bound. Option B (batch size) can increase latency. Option D (warm-up) helps cold starts but not queueing.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
Question Discussion
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